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Internet of Things in Healthcare Applications
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Internet of Things in Healthcare Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 July 2019) | Viewed by 26369

Special Issue Editors

Prof. Dr. Craig Michie

E-Mail Website
Guest Editor
Department of Electronic & Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK
Interests: wireless sensor networks; Internet-of-Things; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Tachtatzis

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, University of Strathclyde, 16 Richmond Street, Glasgow G1 1XQ, UK
Interests: machine learning; partial discharge monitoring; wireless technologies; data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Roma Maguire

E-Mail Website
Guest Editor
Department of Computer and Information Systems, University of Strathclyde, 26 Richmond Street, Glasgow G1 1XH, Scotland, UK
Interests: Digital Health and Care, Supportive Care, Internet of Things, Clinical Decision Making, Remote Patient Monitoring, Real World Evidence

Special Issue Information

Dear Colleagues,

The proliferation of wireless sensor networks has enabled a wide range of healthcare applications to continuously monitor patients and enable the health and social services to become more preventative and anticipatory—keeping people out of hospital care and as well as possible in their own homes. To effectively realise these services, sensors and devices must operate in a non-intrusive fashion for the users and record data in a contextualised, reliable and timely fashion. Furthermore, the large amounts of data collected cannot be screened manually and intelligent decision support technologies are necessary to automatically identify users that require attention and enable professionals to quickly intervene. It is therefore necessary to enable this intelligence at all levels of the technology stack, from the sensor device itself all the way to the data aggregation and visualisation platform. This Special Issue seeks novel articles on:

  • Sensor for physical, physiological, psychological, cognitive, and behavioural processes
  • Systems integration
  • Artificial intelligence and machine learning
  • Internet of Wearable Things
  • Telecare

Prof. Craig Michie
Dr. Christos Tachtatzis
Prof. Roma Maguire
Guest Editors


Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensor for physical, physiological, psychological, cognitive, and behavioural processes
  • Systems integration
  • Artificial intelligence and machine learning
  • Internet of Wearable Things
  • Telecare

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Published Papers (3 papers)

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Research

29 pages, 2226 KiB  
Article
Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals
by Gabriel Souto Fischer, Rodrigo da Rosa Righi, Cristiano André da Costa, Guilherme Galante and Dalvan Griebler
Sensors 2019, 19(17), 3800; https://doi.org/10.3390/s19173800 - 2 Sep 2019
Cited by 8 | Viewed by 4947
Abstract
Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation [...] Read more.
Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively. Full article
(This article belongs to the Special Issue Internet of Things in Healthcare Applications)
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24 pages, 4360 KiB  
Article
IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices
by Argyro Mavrogiorgou, Athanasios Kiourtis, Konstantinos Perakis, Stamatios Pitsios and Dimosthenis Kyriazis
Sensors 2019, 19(9), 1978; https://doi.org/10.3390/s19091978 - 27 Apr 2019
Cited by 66 | Viewed by 9125
Abstract
It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over [...] Read more.
It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy. Full article
(This article belongs to the Special Issue Internet of Things in Healthcare Applications)
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12 pages, 928 KiB  
Article
Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks
by Guto Leoni Santos, Patricia Takako Endo, Kayo Henrique de Carvalho Monteiro, Elisson da Silva Rocha, Ivanovitch Silva and Theo Lynn
Sensors 2019, 19(7), 1644; https://doi.org/10.3390/s19071644 - 6 Apr 2019
Cited by 165 | Viewed by 11217
Abstract
Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus [...] Read more.
Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain. Full article
(This article belongs to the Special Issue Internet of Things in Healthcare Applications)
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